Investors are going to keep challenging firms to show evidence their heavy artificial intelligence investments really are boosting productivity.
That is going to continue being a tough challenge, as history suggests the real output gains will take some time to develop.
So AI "productivity," or the "lack of quantifiable gains," are currently the most significant contemporary case of the Solow productivity paradox.
In 1987, Nobel laureate Robert Solow famously remarked, "You can see the computer age everywhere but in the productivity statistics."
Recent research suggests productivity might actually decline for a time as firms deploy AI.
The reason is the J curve.
“We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains,” say economists Kristina McElheran; Mu-Jeung Yang; Zachary Kroff and Erik Brynjolfsson. “Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding,
while harming productivity and profitability in the short run.”
In other words, it takes time for enterprises to retool their business processes for the new technologies. And the more profound the innovations, perhaps the longer it takes to integrate those tools.
Also, much of the reported AI adoption is horizontal rather than vertical; personal rather than systematic. In other words, individuals might be using chatbots, but workflows have yet to be transformed.
So “personal productivity” has not yet been matched by an applied transformation of key work processes. And personal productivity gains are hard to measure, in terms of impact on firm performance.
Agentic AI should help, as they can affect complex business processes.
Many have noted that U.S. labor productivity significantly slowed in the 1970s and 1980s, despite rapid information technology investment.
Then starting in the mid 1990s a decade of faster growth returned arguably because business process re-engineering had taken place.
A similar productivity paradox surrounds AI. As explained by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson in a 2017 working paper, AI and the Modern Productivity Paradox,” the paradox is primarily due to the time lag between technology advances and their impact on the economy.
While technologies may advance rapidly, humans and our institutions change slowly.
Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.
Translating technological advances into productivity gains requires major transformations, and therefore time.
Today, we see a "Modern AI Paradox": while Large Language Models (LLMs) and Generative AI are ubiquitous in headlines and corporate pilots, global aggregate productivity growth remains sluggish.
Economists like Erik Brynjolfsson argue that the paradox isn't a failure of the technology, but a timing and structural issue. He identifies four main reasons for this lag:
Mismeasurement: AI often improves quality, variety, or speed in ways that traditional GDP (which tracks "units produced") fails to capture.
Redistribution: AI may be used for "rent-seeking" (competing for market share) rather than increasing total industry output.
Implementation Lags: Significant "General Purpose Technologies" (like electricity or the steam engine) require decades of organizational restructuring before they move the needle.
Mismanagement: Companies often use AI to automate old processes rather than inventing new, more efficient business models.
While individual tasks show gains, enterprise-wide productivity often remains flat for several reasons:
The "Pilot Trap": According to recent Adobe/Business research, 86 percent of IT leaders see potential, but only a fraction have moved beyond "isolated experiments" to organization-wide workflows
Inertial Workflows: Companies often use AI to "do the old thing faster" (e.g., writing more emails) rather than "doing the right thing" (e.g., eliminating the need for those emails entirely). This results in "Digital Overload"
The Human Bottleneck: AI can generate a report in seconds, but a human still takes hours to verify, edit, and approve it. Without changing the governance and approval structures, the AI speed gain is neutralized
Data Fragmentation: Most AI models are effective only if they can access clean, centralized data. Most enterprises still have "siloed" data, leading to AI hallucinations or irrelevant outputs
Skills Gap: Enterprises frequently treat AI as a "plug-and-play" tool like a calculator, failing to realize it requires a new type of "AI Literacy" to prompt and integrate effectively into complex projects.
None of that will be too comforting for suppliers who must justify their heavy AI capital investment.
But history suggests the payoff is coming. It just will take some time. It always does.
